A Randomised Primal-Dual Algorithm for Distributed Radio-Interferometric Imaging

Alex Onose, Rafael E. Carrillo, Jason D. McEwen, Yves Wiaux

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Next generation radio telescopes, like the Square Kilometre Array, will acquire an unprecedented amount of data for radio astronomy. The development of fast, parallelisable or distributed algorithms for handling such large-scale data sets is of prime importance. Motivated by this, we investigate herein a convex optimisation algorithmic structure, based on primal-dual forward-backward iterations, for solving the radio interferometric imaging problem. It can encompass any convex prior of interest. It allows for the distributed processing of the measured data and introduces further flexibility by employing a probabilistic approach for the selection of the data blocks used at a given iteration. We study the reconstruction performance with respect to the data distribution and we propose the use of nonuniform probabilities for the randomised updates. Our simulations show the feasibility of the randomisation given a limited computing infrastructure as well as important computational advantages when compared to state-of-the-art algorithmic structures.
Original languageEnglish
Title of host publication2016 Proceedings of the 24th European Signal Processing Conference
Number of pages5
Publication statusAccepted/In press - 30 May 2016
Event24th European Signal Processing Conference 2016 - Hilton Budapest, Budapest, Hungary
Duration: 29 Aug 20162 Sept 2016
Conference number: 24


Conference24th European Signal Processing Conference 2016
Abbreviated titleEUSIPCO 2016


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